The noise with higher energy than speech is catastrophic for HM. So CEM is designed to roughly suppress the noise of WB spectra, which is an encoder-decoder structure. Before being input to the encoder, the WB spectrum \(\mathbf {S}^{\text{WB}}=\text{Cat}(\mathbf {S}^{\text{WB}}_{r},\mathbf {S}^{\text{WB}}_{i})\)  is compressed by a power of 0.23 and is input to the dual-path encoder together with the original spectrum, as shown in Fig. REF . Both the encoder and decoder are comprised of 2D causal convolution, batch normalization [1]}, and PReLU [2]}. Between the encoder and decoder, DPRNN [3]}, [4]} is inserted to model the multidimensional dependencies and Skip Connection concatenates the output of each encoder to the input of the corresponding decoder (red line in Fig. REF ). The output channel of the last decoder is \((\text{C}_\text{A}+2)\) , where 2 is the estimated mask of CEM \(\mathbf {M}^{\text{CEM}}=\text{Cat}(\mathbf {M}^\text{CEM}_{r},\mathbf {M}^\text{CEM}_{i})\) , \(\text{C}_\text{A}\)  is introduced in the next section. The Mask Apply E of CEM is as follows,
\(\mathbf {S}^{\text{WB}^{\prime }} = |\mathbf {S}^{\text{WB}}| \odot \text{tanh}(|\mathbf {M}^{\text{CEM}}|) \odot e^{j(\mathbf {S}^\text{WB}_{\text{phase}}+\mathbf {M}^\text{CEM}_{\text{phase}})}\) 
